Flowmeter data measuring and calculating method

Abstract

The invention discloses a flow meter data measuring and calculating method, relates to the technical field of flow measurement, and is used for solving the problem that the operation efficiency of a whole system is affected due to the fact that calculation resource consumption is too large under complex working conditions. A timestamp is collected, signal energy distribution is detected, a distortion score is calculated according to a time interval difference value and the energy distribution, and an overall distortion index is calculated in combination with real-time waveform characteristics. Distortion detection accuracy is improved and resource consumption is reduced through segmented calculation, and signal segment importance is evaluated by using an entropy weight method for subsequent repair. And a repair algorithm is selected according to the distortion index, errors and harmonic interference counts are detected during dynamic repair, a repair period is calculated, an early warning is generated, and the reliability of data measurement and calculation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flow measurement, and more specifically, to a method for calculating flowmeter data. Background Art

[0002] In practical applications, vehicle-mounted flowmeters often face the problem of data distortion. This distortion may be caused by various factors, such as signal interference, transmission loss, or equipment error, etc., resulting in a decrease in the accuracy and reliability of flow measurement. To solve this problem, various data restoration methods have been proposed in the prior art, which repair and restore the distorted data through algorithms to improve the measurement accuracy. However, the existing data restoration technologies still have certain deficiencies in processing flow data under complex working conditions and are difficult to fully meet the requirements of modern industry for efficient and accurate flow measurement.

[0003] The prior art has the following deficiencies:

[0004] Currently, it mainly focuses on the restoration of network flow data, without fully considering the characteristics of vehicle-mounted flowmeter data. It emphasizes the guarantee of integrity during data acquisition and transmission, but has limited ability to repair data distortion. Especially when dealing with the common non-linear distortion and high-frequency noise in vehicle-mounted flowmeter data, it is unable to achieve high-precision data restoration, resulting in excessive consumption of computing resources under complex working conditions and affecting the overall operation efficiency of the system. Therefore, a method for calculating flowmeter data is proposed.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for calculating flowmeter data. By analyzing the signal fluctuation characteristics of the vehicle-mounted flowmeter and combining with a multi-dimensional data processing algorithm, key characteristic parameters of the distorted data are extracted, and the distorted data is repaired using a dynamic weight allocation mechanism. During the repair process, the data quality index is monitored in real time to generate an optimized flow measurement result. This method solves the problem of data distortion of vehicle-mounted flowmeters mentioned in the background art, especially the problem of insufficient ability to repair high-frequency noise and non-linear distortion under complex working conditions.

[0007] To achieve the above object, the present invention provides the following technical solution, a method for calculating flowmeter data, including the following steps:

[0008] Step S1: Obtain the working signal record of the vehicle-mounted flowmeter, screen out the abnormal signal segments according to the fluctuation amplitude and frequency characteristics in the signal record and mark them; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the vehicle-mounted flowmeter is working, and calculate the signal energy distribution;

[0009] Step S2: Calculate the time interval difference of the signal segments according to the timestamp information of the marked signal segments, calculate the distortion score of the corresponding signal segments by using the entropy weight method through the time interval difference and the signal energy distribution, detect the real-time waveform characteristics of each marked signal segment, and calculate the overall distortion index of the vehicle-mounted flowmeter;

[0010] Step S3: Select to use a static repair algorithm or a dynamic repair algorithm to process the distorted data according to the overall distortion index. After each use of the dynamic repair algorithm for processing, measure the signal repair error and calculate the error change amount, collect the number of peaks in the signal spectrum as the harmonic interference count and record it;

[0011] Step S4: Calculate the quality coefficient of the current signal by using the neural network algorithm based on the signal repair error change amount and the harmonic interference count, adjust the execution period of the dynamic repair algorithm according to the quality coefficient, and generate a warning prompt.

[0012] In a preferred embodiment, in step S1, call the working signal record of the vehicle-mounted flowmeter through the historical database. Each signal record is the signal amplitude and frequency characteristics of the vehicle-mounted flowmeter under normal working conditions in the historical data, and use the signal amplitude and frequency characteristics of the vehicle-mounted flowmeter under normal working conditions as the reference signal segments;

[0013] Record the amplitude and frequency characteristics of all reference signal segments in the vehicle-mounted flowmeter and calculate the average value as the signal screening threshold, screen out the signal segments that exceed the signal screening threshold and mark them.

[0014] In a preferred embodiment, in step S1, select a period of time as the analysis window when the vehicle-mounted flowmeter is working, and use the signal acquisition module to detect the amplitude change of the input signal when the vehicle-mounted flowmeter is working within the analysis window;

[0015] Calculate the signal energy distribution according to the analysis window and the amplitude change of the input signal: E = A 2 ×t;

[0016] where A is the amplitude of the input signal when the vehicle-mounted flowmeter is working, t is the time interval of the analysis window, and E is the energy distribution of the corresponding signal segment;

[0017] Randomly select two time points within the analysis window to detect the timestamp information of each marked signal segment.

[0018] In a preferred embodiment, in step S2, the time interval difference of the marked signal segment is obtained by subtracting the time stamp information detected at the latter time point from the time stamp information detected at the former time point within the analysis window;

[0019] Calculate the distortion growth rate coefficient of each marked signal segment according to the time interval difference of the marked signal segment and the signal energy distribution, and use it as the distortion score of each marked signal segment;

[0020] Calculate the distortion growth rate coefficient of the marked signal segment: C = Δt × E;

[0021] Where, Δt is the time interval difference of the marked signal segment, E is the signal energy distribution of the marked signal segment, C is the distortion growth rate coefficient corresponding to the marked signal segment, and i is the serial number of each marked signal segment;

[0022] Use the signal analysis module to detect the real-time waveform characteristics of each marked signal segment, use the distortion growth rate coefficient of each marked signal segment as the distortion score, sort the distortion scores from small to large, and set the distortion weight for each sorted marked signal segment by using the entropy weight method.

[0023] In a preferred embodiment, in step S2, use the entropy weight method to set the distortion weight and calculate the overall distortion index of the entire vehicle-mounted flowmeter in combination with the real-time waveform characteristics of each marked signal segment;

[0024] The specific steps are as follows: construct a distortion matrix through the distortion scores of each marked signal segment to compare the relative importance between signal segments;

[0025] Perform normalization processing on each column of the distortion matrix so that the sum of each column is equal to 1;

[0026] Calculate the weight value of each factor. The weight value is the weighted average of each column of the distortion matrix, and use the weight value of each signal segment as the quantization value of the importance of each signal segment;

[0027] Use the weight value of each signal segment as the distortion weight of the corresponding marked signal segment, and perform weighted summation of the distortion weight of each marked signal segment and the real-time waveform characteristics of the corresponding marked signal segment to obtain the overall distortion index of the entire vehicle-mounted flowmeter.

[0028] In a preferred embodiment, in step S3, when the overall distortion index of the vehicle-mounted flowmeter is lower than the distortion index threshold, use the static repair algorithm to process the distorted data;

[0029] When the overall distortion index of the vehicle-mounted flowmeter exceeds the distortion index threshold, use the dynamic repair algorithm to process the distorted data.

[0030] In a preferred embodiment, in step S3, after each data processing is completed, the error analysis module is used to measure and record the signal repair error, and the difference between the signal repair error measured this time and the signal repair error recorded last time is calculated to obtain the change amount of the signal repair error;

[0031] The signal spectrum is collected according to the set fixed sampling frequency, and the peaks exceeding the preset amplitude are obtained and the number of them is counted as the harmonic interference count and recorded.

[0032] In a preferred embodiment, in step S4, the quality coefficient of the current signal is calculated by using the neural network algorithm based on the change amount of the signal repair error and the harmonic interference count: Q = k / (w1 × Δe + w2 × N);

[0033] Where Q is the quality coefficient of the current signal, Δe is the change amount of the signal repair error after this data processing, N is the harmonic interference count after this data processing, w1 and w2 are the influence weights of the two parameters respectively, and k is the adjustment constant;

[0034] Take the reciprocal of the quality coefficient of the current signal as the execution cycle adjustment ratio of the next dynamic repair algorithm, and take the product of the execution cycle of the current dynamic repair algorithm and the execution cycle adjustment ratio as the execution cycle of the next dynamic repair algorithm.

[0035] In a preferred embodiment, in step S4, the execution cycle is recorded in real time each time the dynamic repair algorithm is used to process the distorted data. When the processing time reaches the execution cycle of this time, the static repair algorithm is switched to process the data of the vehicle-mounted flowmeter;

[0036] If the execution cycle of the next dynamic repair algorithm calculated from the execution cycle of the current dynamic repair algorithm is lower than the preset cycle threshold, a warning is given and it is prompted to replace the signal acquisition module.

[0037] The technical effects and advantages of the present invention:

[0038] 1. The present invention screens and marks abnormal signal segments of an on-vehicle flowmeter by obtaining the working signal records of the on-vehicle flowmeter, collects the timestamp information of the marked signal segments, and detects the signal energy distribution when the marked signal segments are working. The distortion scores of each marked signal segment are calculated according to the time interval difference and the signal energy distribution of each marked signal segment. The overall distortion index of the on-vehicle flowmeter is calculated according to the distortion scores of each marked signal segment and the real-time waveform characteristics of each marked signal segment. By calculating the overall distortion index of the on-vehicle flowmeter in segments, the consumption of signal processing resources is reduced while the accuracy of distortion detection is improved. And the relative importance of each signal segment is analyzed by the entropy weight method, which is convenient for subsequent targeted repair. Different repair algorithms are selected according to the overall distortion index to process the distorted data. When the dynamic repair algorithm is used, the signal repair error and the harmonic interference count are detected, the execution period of the dynamic repair algorithm is calculated, and a warning prompt is generated, thereby improving the reliability of the data measurement of the on-vehicle flowmeter. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall process of a method for measuring flowmeter data according to the present invention.

[0040] Figure 2 It is a logic block diagram of the distortion score calculation process in a method for measuring flowmeter data according to the present invention.

[0041] Figure 3 It is a schematic diagram of the structure of the dynamic repair algorithm execution period adjustment mechanism in a method for measuring flowmeter data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1

[0044] Please refer to Figures 1 to 3 , a method for measuring flowmeter data, and the specific operation process is as follows:

[0045] Step S1: Obtain the working signal records of the on-vehicle flowmeter, screen out and mark the abnormal signal segments according to the fluctuation amplitude and frequency characteristics in the signal records; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the on-vehicle flowmeter is working, and calculate the signal energy distribution;

[0046] Step S2: Calculate the time interval difference of the signal segments based on the timestamp information of the marked signal segments, calculate the distortion score of the corresponding signal segments using the entropy weight method through the time interval difference and the signal energy distribution, detect the real-time waveform characteristics of each marked signal segment, and calculate the overall distortion index of the vehicle-mounted flowmeter;

[0047] Step S3: Select to use a static repair algorithm or a dynamic repair algorithm to process the distorted data according to the overall distortion index. After each use of the dynamic repair algorithm for processing, measure the signal repair error and calculate the error change amount, collect the number of peaks in the signal spectrum as the harmonic interference count and record it;

[0048] Step S4: Calculate the quality coefficient of the current signal using a neural network algorithm by integrating the signal repair error change amount and the harmonic interference count, and adjust the execution period of the dynamic repair algorithm according to the quality coefficient and generate a warning prompt.

[0049] In practical applications, the above method is implemented through the following modules to complete the measurement and repair of flowmeter data through the coordinated cooperation of a signal acquisition module, a distortion score calculation module, a dynamic repair module, a static repair module, a quality coefficient calculation module, and a warning prompt module.

[0050] First, the signal acquisition module is used to obtain the working signal record of the vehicle-mounted flowmeter and perform a preliminary screening on it.

[0051] In the specific implementation process, the signal acquisition module calls the signal amplitude and frequency characteristics of the vehicle-mounted flowmeter in the normal working state from the historical database as the reference signal segments, calculates the average value after statistically analyzing the amplitude and frequency characteristics of these reference signal segments, and uses it as the signal screening threshold.

[0052] When the vehicle-mounted flowmeter is running, the signal acquisition module real-time collects the amplitude change of the current signal, compares it with the signal screening threshold, and marks the abnormal signal segments that exceed the threshold.

[0053] At the same time, the signal acquisition module also records the timestamp information of each abnormal signal segment, and calculates the signal energy distribution by detecting the amplitude change of the input signal through an analysis window.

[0054] The time interval of the analysis window is set by the user, usually selected between 0.1 second and 1 second to ensure that the influence of high-frequency noise can be captured.

[0055] The calculation formula for the signal energy distribution is E = A 2 ×t, where A is the input signal amplitude, t is the time interval of the analysis window, and E is the energy distribution value of the corresponding signal segment.

[0056] Through the above operations of the signal acquisition module, abnormal signal segments that need further processing can be preliminarily screened out, providing basic data for subsequent steps.

[0057] Next, the distortion score calculation module further analyzes the abnormal signal segments marked by the signal acquisition module and calculates their distortion scores.

[0058] The distortion score calculation module first calculates the time interval difference based on the timestamp information of the marked signal segments, and then combines the signal energy distribution to calculate the distortion growth rate coefficient as the distortion score of each marked signal segment.

[0059] Specifically, two time points are randomly selected within the analysis window to detect the timestamp information of each marked signal segment, and the time interval difference Δt is obtained by subtracting the timestamp of the previous time point from the timestamp of the subsequent time point.

[0060] Subsequently, the distortion score calculation module uses the formula C = Δt × E to calculate the distortion growth rate coefficient C, where Δt is the time interval difference and E is the signal energy distribution.

[0061] The distortion growth rate coefficient C reflects the growth rate of signal distortion, and the larger the value, the more serious the distortion. The distortion score calculation module sorts the distortion growth rate coefficients of all marked signal segments from small to large and uses the entropy weight method to set the distortion weights.

[0062] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix, form a matrix through the distortion scores of each marked signal segment and perform normalization processing on each column so that the sum of each column is equal to 1;

[0063] Then calculate the weight value of each factor, and the weight value is the weighted average of each column of the distortion matrix. Finally, the weight values of each signal segment are used as their importance quantification values.

[0064] Finally, the distortion score calculation module performs weighted summation of the distortion weights of each marked signal segment and its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle-mounted flowmeter.

[0065] The overall distortion index comprehensively reflects the distortion degree of the vehicle-mounted flowmeter, providing a basis for subsequent selection of repair algorithms.

[0066] After determining the overall distortion index, the dynamic repair module and the static repair module select different repair strategies according to the size of the distortion index.

[0067] If the overall distortion index is lower than the preset distortion index threshold, the static repair module is enabled to process the distorted data; if the overall distortion index is higher than or equal to the distortion index threshold, the dynamic repair module is enabled for repair.

[0068] After each repair is completed, the dynamic repair module measures and records the signal repair error through the error analysis module.

[0069] The error analysis module subtracts the signal repair error measured this time from the signal repair error recorded last time to obtain the signal repair error change Δe.

[0070] Meanwhile, the dynamic repair module also collects the signal spectrum according to the set fixed sampling frequency, and extracts the number of peaks exceeding the preset amplitude as the harmonic interference count N and records it.

[0071] The signal repair error change and the harmonic interference count are important indicators for evaluating the repair effect, and they jointly determine the execution cycle adjustment ratio of the dynamic repair algorithm.

[0072] The quality coefficient calculation module is responsible for integrating the signal repair error change and the harmonic interference count, and calculating the quality coefficient Q of the current signal using the neural network algorithm.

[0073] The calculation formula for the quality coefficient is Q = k / (w1×Δe + w2×N), where k is an adjustment constant, and w1 and w2 are the influence weights of the signal repair error change and the harmonic interference count respectively.

[0074] The reciprocal of the quality coefficient Q is used as the execution cycle adjustment ratio for the next dynamic repair algorithm, that is, the next execution cycle is equal to the current execution cycle multiplied by 1 / Q.

[0075] The dynamic repair module adjusts the execution cycle according to the calculation result to ensure that the repair process can maintain high efficiency and accuracy under different distortion degrees.

[0076] In addition, the dynamic repair module also records the execution cycle of each repair in real time, and switches to the static repair module for processing when the processing time reaches the current execution cycle.

[0077] If the next execution cycle calculated from the execution cycle of the current dynamic repair algorithm is lower than the preset cycle threshold, the warning prompt module issues a warning and prompts to replace the signal acquisition module.

[0078] In the whole process, the connection relationship and cooperation method between each module are crucial.

[0079] The signal acquisition module transfers the marked abnormal signal segment and its timestamp information to the distortion score calculation module, and the latter provides a decision basis for the dynamic repair module and the static repair module by calculating the distortion score and the overall distortion index.

[0080] The dynamic repair module and the static repair module select appropriate repair strategies according to the overall distortion index, and feedback the repair results to the quality coefficient calculation module.

[0081] The quality coefficient calculation module calculates the quality coefficient through a neural network algorithm and passes the result to the dynamic repair module to adjust the execution cycle.

[0082] The early warning prompt module then determines whether to issue an early warning based on the execution cycle of the dynamic repair module, thus forming a closed-loop control system.

[0083] This modular design not only improves the flexibility of the system but also ensures the efficient cooperation between modules.

[0084] In practical applications, it is assumed that the on-vehicle flowmeter is installed on a heavy truck driving on complex road conditions. Due to the long-term operation of the vehicle, the signal of the on-vehicle flowmeter is affected by factors such as road surface bumps, engine vibrations, and electromagnetic interference, resulting in high-frequency noise and non-linear distortion in the signal.

[0085] At this time, the flowmeter data calculation method provided by the present invention processes the signal step by step through modular design to ensure that the final flow measurement result has high accuracy and stability.

[0086] First of all, the signal acquisition module starts to work, calls the signal amplitude and frequency characteristics of the on-vehicle flowmeter under normal conditions from the historical database as the reference signal segment, and calculates the average value of these reference signal segments to determine the signal screening threshold.

[0087] When the on-vehicle flowmeter is running, the signal acquisition module real-time collects the amplitude change of the current signal and compares it with the signal screening threshold to mark the abnormal signal segments exceeding the threshold.

[0088] At the same time, the signal acquisition module records the timestamp information of each abnormal signal segment and calculates the signal energy distribution by detecting the amplitude change of the input signal through the analysis window.

[0089] For example, select a time interval of 0.5 seconds within the analysis window, and calculate the signal energy distribution value E in combination with the formula E = A 2 ×t.

[0090] At this time, if the amplitude A of a certain signal segment is 2 volts and the time interval t is 0.5 seconds, then the energy distribution value E of this signal segment is 2 2 ×0.5 = 2 joules.

[0091] In this way, the signal acquisition module can initially screen out the abnormal signal segments that need further processing and pass the timestamp information of these signal segments to the distortion score calculation module.

[0092] Next, the distortion score calculation module further analyzes according to the abnormal signal segments marked by the signal acquisition module.

[0093] Randomly select two time points within the analysis window, denoted as t1 and t2 respectively, and calculate the time interval difference Δt = t2 - t1.

[0094] For example, if t1 is 0.2 seconds and t2 is 0.7 seconds, then Δt = 0.7 - 0.2 = 0.5 seconds.

[0095] Subsequently, the distortion score calculation module calculates the distortion growth rate coefficient C using the formula C = Δt × E.

[0096] For example, if Δt is 0.5 seconds and the signal energy distribution E is 2 joules, then the distortion growth rate coefficient C = 0.5 × 2 = 1.

[0097] The distortion growth rate coefficient C reflects the growth rate of signal distortion, and the larger the value, the more serious the distortion.

[0098] The distortion score calculation module sorts the distortion growth rate coefficients of all marked signal segments from smallest to largest and sets the distortion weights using the entropy weight method.

[0099] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix, form a matrix through the distortion scores of each marked signal segment and perform normalization processing on each column so that the sum of each column is equal to 1;

[0100] Then calculate the weight value of each factor. The weight value is the weighted average of each column of the distortion matrix, and finally, the weight values of each signal segment are used as their importance quantification values.

[0101] Finally, the distortion score calculation module performs a weighted sum of the distortion weights of each marked signal segment and its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle-mounted flowmeter.

[0102] For example, if the distortion weight of a certain signal segment is 0.3 and the real-time waveform characteristic is 0.5, then the comprehensive score of this signal segment is 0.3 × 0.5 = 0.15.

[0103] In this way, the distortion score calculation module can comprehensively evaluate the distortion degree of the vehicle-mounted flowmeter and transmit the overall distortion index to the dynamic repair module and the static repair module.

[0104] In an actual application scenario, assume that the vehicle-mounted flowmeter is installed on a vehicle traveling on a complex road condition. Due to factors such as road surface bumps and engine vibrations, high-frequency noise and non-linear distortion occur in the flowmeter signal.

[0105] At this time, the signal acquisition module first calls the signal characteristics in the normal working state through the historical database, combines the current signal amplitude changes to screen out abnormal signal segments and marks their timestamp information.

[0106] The distortion score calculation module calculates the distortion growth rate coefficient based on the time interval difference of the marked signal segments and the signal energy distribution, and sets the distortion weight using the entropy weight method to finally obtain the overall distortion index.

[0107] If the overall distortion index is high, the dynamic repair module is activated and adjusts the execution cycle according to the signal repair error change amount and the harmonic interference count to gradually repair the distorted data.

[0108] During this process, the quality coefficient calculation module continuously optimizes the repair strategy, while the warning prompt module monitors the system status in real time to ensure the reliability of the flowmeter data calculation.

[0109] Through the above process, the present invention effectively solves the problem of data distortion of in-vehicle flowmeters under complex working conditions, and significantly improves the accuracy and stability of flow measurement.

[0110] In order to better enable relevant personnel in the technical field to fully understand and implement the present invention, the following further supplements and explains the specific implementation principle of the present invention in combination with a specific application scenario.

[0111] In practical applications, it is assumed that the in-vehicle flowmeter is installed on a heavy truck driving on a complex road condition.

[0112] Due to the long-term operation of the vehicle, the in-vehicle flowmeter signal is affected by factors such as road surface bumps, engine vibrations, and electromagnetic interference, resulting in high-frequency noise and nonlinear distortion of the signal.

[0113] At this time, the flowmeter data calculation method provided by the present invention processes the signal step by step through modular design to ensure that the final flow measurement result has high accuracy and stability.

[0114] First, the signal acquisition module starts to work, calls the signal amplitude and frequency characteristics of the in-vehicle flowmeter in the normal state from the historical database as the reference signal segments, and calculates the average value of these reference signal segments to determine the signal screening threshold.

[0115] When the in-vehicle flowmeter is running, the signal acquisition module real-time collects the amplitude change of the current signal, and compares it with the signal screening threshold to mark the abnormal signal segments exceeding the threshold.

[0116] At the same time, the signal acquisition module records the timestamp information of each abnormal signal segment, and calculates the signal energy distribution by detecting the amplitude change of the input signal through the analysis window.

[0117] For example, select a time interval of 0.5 seconds within the analysis window, and calculate the signal energy distribution value E in combination with the formula E = A 2 ×t.

[0118] At this time, if the amplitude A of a certain signal segment is 2 volts and the time interval t is 0.5 seconds, then the energy distribution value E of this signal segment is 2 2 ×0.5 = 2 joules.

[0119] In this way, the signal acquisition module can initially screen out abnormal signal segments that need further processing and transmit the timestamp information of these signal segments to the distortion score calculation module.

[0120] Next, the distortion score calculation module performs further analysis based on the abnormal signal segments marked by the signal acquisition module.

[0121] Randomly select two time points within the analysis window, denoted as t1 and t2 respectively, and calculate the time interval difference Δt = t2 - t1.

[0122] For example, if t1 is 0.2 seconds and t2 is 0.7 seconds, then Δt = 0.7 - 0.2 = 0.5 seconds.

[0123] Subsequently, the distortion score calculation module calculates the distortion growth rate coefficient C using the formula C = Δt × E.

[0124] For example, if Δt is 0.5 seconds and the signal energy distribution E is 2 joules, then the distortion growth rate coefficient C = 0.5 × 2 = 1.

[0125] The distortion growth rate coefficient C reflects the growth rate of signal distortion, and the larger the value, the more serious the distortion.

[0126] The distortion score calculation module sorts the distortion growth rate coefficients of all marked signal segments from smallest to largest and sets the distortion weights using the entropy weight method.

[0127] The specific implementation process of the entropy weight method is as follows: First, construct a distortion matrix, form a matrix through the distortion scores of each marked signal segment and perform normalization processing on each column so that the sum of each column is equal to 1;

[0128] Then calculate the weight value of each factor, and the weight value is the weighted average of each column of the distortion matrix. Finally, take the weight values of each signal segment as their importance quantification values.

[0129] Finally, the distortion score calculation module performs a weighted sum of the distortion weights of each marked signal segment and its real-time waveform characteristics to obtain the overall distortion index of the entire vehicle-mounted flowmeter.

[0130] For example, if the distortion weight of a certain signal segment is 0.3 and the real-time waveform characteristic is 0.5, then the comprehensive score of this signal segment is 0.3 × 0.5 = 0.15.

[0131] In this way, the distortion score calculation module can comprehensively evaluate the distortion degree of the vehicle-mounted flowmeter and transmit the overall distortion index to the dynamic repair module and the static repair module.

[0132] After determining the overall distortion index, the dynamic repair module and the static repair module select different repair strategies according to the magnitude of the distortion index.

[0133] For example, if the overall distortion index is lower than the preset distortion index threshold (such as 0.8), the static repair module is enabled to process the distorted data; if the overall distortion index is higher than or equal to the distortion index threshold, the dynamic repair module is enabled for repair.

[0134] After each repair is completed, the dynamic repair module measures and records the signal repair error through the error analysis module.

[0135] The error analysis module subtracts the signal repair error measured this time from the signal repair error recorded last time to obtain the signal repair error change Δe.

[0136] For example, if the signal repair error recorded last time is 0.05 and the signal repair error measured this time is 0.03, then Δe = 0.05 - 0.03 = 0.02.

[0137] At the same time, the dynamic repair module also collects the signal spectrum according to the set fixed sampling frequency, extracts the number of peaks exceeding the preset amplitude as the harmonic interference count N and records it.

[0138] For example, if the number of peaks exceeding the preset amplitude in the signal spectrum is 3, then the harmonic interference count N = 3.

[0139] The signal repair error change and the harmonic interference count are important indicators for evaluating the repair effect, and they jointly determine the adjustment ratio of the execution cycle of the dynamic repair algorithm.

[0140] The quality coefficient calculation module is responsible for comprehensively considering the signal repair error change and the harmonic interference count, and uses the neural network algorithm to calculate the quality coefficient Q of the current signal.

[0141] The calculation formula for the quality coefficient is Q = k / (w1×Δe + w2×N), where k is an adjustment constant (such as 10), and w1 and w2 are the influence weights of the signal repair error change and the harmonic interference count respectively (such as w1 = 0.6, w2 = 0.4).

[0142] For example, if Δe = 0.02 and N = 3, then Q = 10 / (0.6×0.02 + 0.4×3) = 10 / (0.012 + 1.2) ≈ 8.26.

[0143] The reciprocal of the quality factor Q is used as the adjustment ratio of the execution period for the next dynamic repair algorithm, that is, the next execution period is equal to the current execution period multiplied by 1 / Q.

[0144] For example, if the current execution period is 1 second, then the next execution period is 1×(1 / 8.26)≈0.12 seconds.

[0145] The dynamic repair module adjusts the execution period according to the calculation result to ensure that the repair process can maintain high efficiency and accuracy under different distortion degrees.

[0146] In addition, the dynamic repair module also records the execution period of each repair in real time, and switches to the static repair module for processing when the processing time reaches the current execution period.

[0147] If the next execution period calculated from the execution period of the current dynamic repair algorithm is lower than the preset period threshold (such as 0.1 second), the warning prompt module issues a warning and prompts to replace the signal acquisition module.

[0148] In the whole process, the connection relationship and cooperation mode between each module are crucial.

[0149] The signal acquisition module transmits the marked abnormal signal segment and its timestamp information to the distortion score calculation module, and the latter provides decision-making basis for the dynamic repair module and the static repair module by calculating the distortion score and the overall distortion index.

[0150] The dynamic repair module and the static repair module select appropriate repair strategies according to the overall distortion index, and feedback the repair results to the quality factor calculation module.

[0151] The quality factor calculation module calculates the quality factor through the neural network algorithm and transmits the result to the dynamic repair module to adjust the execution period.

[0152] The warning prompt module judges whether to issue a warning according to the execution period of the dynamic repair module, thus forming a closed-loop control system.

[0153] This modular design not only improves the flexibility of the system, but also ensures the efficient cooperation between each module.

[0154] Through the above process, the present invention effectively solves the problem of data distortion of the vehicle-mounted flowmeter under complex working conditions.

[0155] For example, during vehicle driving, due to road surface bumps, high-frequency noise appears in the flowmeter signal. The signal acquisition module can quickly screen out abnormal signal segments. The distortion score calculation module accurately locates the distortion degree by calculating the distortion growth rate coefficient and the overall distortion index. The dynamic repair module optimizes the repair strategy according to the signal repair error change amount and the harmonic interference count. The quality coefficient calculation module dynamically adjusts the execution period through the neural network algorithm, ultimately ensuring the accuracy and stability of the flow measurement result.

[0156] This process significantly improves the data processing ability of in-vehicle flowmeters under complex working conditions, meeting the requirements of modern industry for efficient and accurate flow measurement.

[0157] The above formulas are all calculated by taking the numerical value without dimension. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0158] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0159] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0160] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0161] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0162] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0163] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0165] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0166] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A method for calculating flowmeter data, characterized in that: Including the following steps: Step S1: Obtain the working signal record of the vehicle-mounted flowmeter, screen out abnormal signal segments according to the fluctuation amplitude and frequency characteristics in the signal record and mark them; collect the timestamp information of the marked signal segments, detect the amplitude change of the input signal when the vehicle-mounted flowmeter is working, and calculate the signal energy distribution; Step S2: Calculate the time interval difference of the signal segments according to the timestamp information of the marked signal segments, calculate the distortion score of the corresponding signal segments by using the entropy weight method through the time interval difference and the signal energy distribution, detect the real-time waveform characteristics of each marked signal segment, and calculate the overall distortion index of the vehicle-mounted flowmeter; Step S3: Select to use a static repair algorithm or a dynamic repair algorithm to process the distorted data according to the overall distortion index. After each use of the dynamic repair algorithm for processing, measure the signal repair error and calculate the error change amount, collect the number of peaks in the signal spectrum as the harmonic interference count and record it; Step S4: Calculate the quality coefficient of the current signal by using the neural network algorithm based on the signal repair error change amount and the harmonic interference count, adjust the execution period of the dynamic repair algorithm according to the quality coefficient, and generate a warning prompt.

2. A method for measuring flowmeter data according to claim 1, characterized in that: In step S1, call the working signal record of the vehicle-mounted flowmeter through the historical database. Each signal record is the signal amplitude and frequency characteristics of the vehicle-mounted flowmeter under normal working conditions in the historical data, and use the signal amplitude and frequency characteristics of the vehicle-mounted flowmeter under normal working conditions as the reference signal segment; Record the amplitude and frequency characteristics of all reference signal segments in the vehicle-mounted flowmeter and calculate the average value as the signal screening threshold, screen out the signal segments exceeding the signal screening threshold and mark them.

3. A method for measuring flowmeter data according to claim 2, characterized in that: In step S1, select a period of time as the analysis window when the vehicle-mounted flowmeter is working, and use the signal acquisition module to detect the amplitude change of the input signal when the vehicle-mounted flowmeter is working within the analysis window; Calculate the signal energy distribution according to the analysis window and the amplitude change of the input signal: E = A 2 ×t; Wherein, A is the amplitude of the input signal when the vehicle-mounted flowmeter is working, t is the time interval of the analysis window, and E is the energy distribution of the corresponding signal segment; Randomly select two time points within the analysis window to detect the timestamp information of each marked signal segment.

4. A method for measuring flowmeter data according to claim 3, characterized in that: In step S2, within the analysis window, subtract the timestamp information detected at the previous time point from the timestamp information detected at the latter time point to obtain the time interval difference of the marked signal segment; Calculate the distortion growth rate coefficient of each marked signal segment according to the time interval difference and the signal energy distribution of the marked signal segment and use it as the distortion score of each marked signal segment; Calculate the distortion growth rate coefficient of the marked signal segment: C = Δt × E; Wherein, Δt is the time interval difference of the marked signal segment, E is the signal energy distribution of the marked signal segment, C is the distortion growth rate coefficient of the corresponding marked signal segment, and i is the serial number of each marked signal segment; The real-time waveform features of each marked signal segment are detected by using a signal analysis module. The distortion growth rate coefficient of each marked signal segment is used as the distortion score. The distortion scores are sorted from small to large, and the entropy weight method is used to set the distortion weights for each sorted marked signal segment.

5. A method for calculating flowmeter data according to claim 4, characterized in that: In step S2, the entropy weight method is used to set the distortion weights and combined with the real-time waveform features of each marked signal segment to calculate the overall distortion index of the entire vehicle-mounted flowmeter; The specific steps are as follows: construct a distortion matrix through the distortion scores of each marked signal segment to compare the relative importance between signal segments; Perform normalization processing on each column of the distortion matrix so that the sum of each column is equal to 1; Calculate the weight value of each factor. The weight value is the weighted average of each column of the distortion matrix, and the weight values of each signal segment are used as the quantization values of the importance of each signal segment; The weight values of each signal segment are used as the distortion weights of the corresponding marked signal segments, and the weighted sum of the distortion weights of each marked signal segment and the real-time waveform features of the corresponding marked signal segments is used to obtain the overall distortion index of the entire vehicle-mounted flowmeter.

6. A method for calculating flowmeter data according to claim 1, characterized in that: In step S3, when the overall distortion index of the vehicle-mounted flowmeter is lower than the distortion index threshold, the static repair algorithm is used to process the distorted data; When the overall distortion index of the vehicle-mounted flowmeter exceeds the distortion index threshold, the dynamic repair algorithm is used to process the distorted data.

7. A method for calculating flowmeter data according to claim 6, characterized in that: In step S3, after each data processing is completed, the signal repair error is measured by using an error analysis module and recorded, and the difference between the signal repair error measured this time and the signal repair error recorded last time is used to obtain the signal repair error change; Collect the signal spectrum according to the set fixed sampling frequency, obtain the peaks exceeding the preset amplitude and count their number as the harmonic interference count and record it.

8. A method for calculating flowmeter data according to claim 7, characterized in that: In step S4, the quality coefficient of the current signal is calculated by using the neural network algorithm by integrating the signal repair error change and the harmonic interference count: Q = k / (w1×Δe + w2×N); Where, Q is the quality coefficient of the current signal, Δe is the signal repair error change after this data processing, N is the harmonic interference count after this data processing, w1 and w2 are the influence weights of two parameters respectively, and k is an adjustment constant; Take the reciprocal of the quality coefficient of the current signal as the execution cycle adjustment ratio of the next dynamic repair algorithm, and take the product of the execution cycle of the current dynamic repair algorithm and the execution cycle adjustment ratio as the execution cycle of the next dynamic repair algorithm.

9. A method for calculating flowmeter data according to claim 8, characterized in that: In step S4, when using the dynamic repair algorithm to process the distorted data each time, the execution cycle is recorded in real time. When the processing time reaches the execution cycle of this time, switch to the static repair algorithm to process the data of the vehicle-mounted flowmeter; If the execution period of the next dynamic repair algorithm calculated from the execution period of the current dynamic repair algorithm is lower than the preset period threshold, a warning is issued and a prompt is given to replace the signal acquisition module.

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